Search Results (2,759)

Reservoir group flood control scheduling decision-making faces multiple uncertainties, such as dynamic fluctuations of evaluation indicators and conflicts in weight assignment. This study proposes a risk analysis model for the decision-making process: capturing the temporal uncertainties of flood control indicators (such as reservoir maximum water level and downstream control section flow) through the Long Short-Term Memory (LSTM) network, constructing a feasible weight space including four scenarios (unique fixed value, uniform distribution, etc.), resolving conflicts among the weight results from four methods (Analytic Hierarchy Process (AHP), Entropy Weight, Criteria Importance Through Intercriteria Correlation (CRITIC), Principal Component Analysis (PCA)) using game theory, defining decision-making risk as the probability that the actual safety level fails to reach the evaluation threshold, and quantifying risks based on the First-Order Second-Moment (FOSM) method. Case verification in the cascade reservoirs of the Qiantang River Basin of China shows that the model provides a risk assessment framework integrating multi-source uncertainties for flood control scheduling decisions through probabilistic description of indicator uncertainties (e.g., Zmax1 with μ = 65.3 and σ = 8.5) and definition of weight feasible regions (99% weight distribution covered by the 3σ criterion), filling the methodological gap in risk quantification during the decision-making process in existing research. Full article

Global context information is essential for semantic segmentation of remote sensing (RS) images. Due to their remarkable capability to capture global context information and model long-range dependencies, vision transformers have demonstrated great performance on semantic segmentation. However, the high computational complexity of vision transformers impedes their broad application in resource-constrained environments for RS image segmentation. To address this challenge, we propose multi-faceted adaptive token pruning (MATP) to reduce computational cost while maintaining relatively high accuracy. MATP is designed to prune well-learned tokens which do not have a close relation to other tokens. To quantify these two metrics, MATP employs multi-faceted scores: entropy, to evaluate the learning progression of tokens; and attention weight, to assess token correlations. Specially, MATP utilizes adaptive criteria for each score that are automatically adjusted based on specific input features. A token is pruned only when both criteria are satisfied. Overall, MATP facilitates the utilization of vision transformers in resource-constrained environments. Experiments conducted on three widely used datasets reveal that MATP reduces the computation cost about 67–70% with about 3–6% accuracy degradation, achieving a superior trade-off between accuracy and computational cost compared to the state of the art. Full article

Fruits and vegetables are crucial components of a healthy diet, which are susceptible to pests. Therefore, the application of pesticides is a basic manner of crop chemical protection. The aim of this study was a comprehensive analysis of pesticide occurrence in 1114 samples of fruits and vegetables. A unified multi-analytical protocol was used composed of primary–secondary amine/graphitized carbon black/magnesium sulfate to purify samples with diversified profile of interfering substances. Moreover, the obtained analytical data were used to evaluate the critical acute health risk in subpopulations of children and adults within European limits criteria. Out of 550 pesticides analyzed, 38 and 69 compounds were noted in 58.6% of fruits and 44.2% of vegetables, respectively. Acetamiprid (14.1% of all detections) and captan (11.3%) occurred the most frequently in fruits, while pendimethalin (10.6%) and azoxystrobin (8.6%) occurred the most frequently in vegetables. A total of 28% of vegetable and 43% of fruit samples were multiresidues with up to 13 pesticides in dill, reaching a final concentration of 0.562 mg kg⁻¹. Maximum residue level (MRL) was exceeded in 7.9% of fruits and 7.3% of vegetables, up to 7900% MRL for chlorpyrifos in dill (0.79 mg kg⁻¹). Notably, 8 out of 38 pesticides found in fruits (21%; 1.2% for carbendazim) and 24 out of 69 compounds in vegetables (35%, 7.4% for chlorpyrifos) were not approved in the EU. Concentrations of pesticides exceeding MRL were used to assess acute health risk for children and adults. Moreover, the incidence of acute health risk was proved for children consuming parsnip with linuron (156%). In other cases, it was below 100%, indicating that Polish food is safe. The work provides reliable and representative scientific data on the contamination of fruits and vegetables with pesticides. It highlights the importance of legislative changes to avoid the occurrence of not approved pesticides in the EU, increasing food and health safety. Full article

There is a growing global interest in making existing buildings more energy-efficient. However, stakeholders seem to have differing views on the matter, especially in developing countries, thus raising the issue of awareness amongst key stakeholders at the operational stage of existing buildings. This study aimed to examine stakeholders’ awareness of the benefits of passive retrofit in residential buildings using a convergent mixed-methods approach. Quantitative data were collected from 118 property managers and 163 owners of residential buildings, and qualitative data were collected from six government officials in Lagos State, Nigeria. The quantitative data collected were analysed using fuzzy synthetic evaluation, which addresses the fuzziness in judgement-making on multi-criteria phenomena. The results revealed that property managers and owners had a moderately high level of awareness of the environmental, economic, and social benefits of the passive retrofitting of residential buildings. However, while property managers generally had a higher level of awareness than owners, a significant gap was found in their awareness of environmental benefits. Conversely, the qualitative analysis results showed that government officials demonstrated a strong awareness of environmental benefits (energy reduction, air quality, and natural lighting) and economic advantages (cost savings and lower implementation costs). In contrast, their awareness of social benefits was limited to health improvements. The findings have practical implications for policy development and awareness campaigns. Building agencies need to further reinforce their targeted awareness programmes for owners, who demonstrated fair awareness of environmental benefits while leveraging the intermediary role of property managers in promoting home retrofit practices. Economic benefits should also be an integral part of policy frameworks to drive wider adoption across all stakeholder groups. Full article

Precise evaluation of risk and reliability is crucial for decision making and predicting the outcome of investment in a photovoltaic power system (PVPS) due to its intermittent source. This paper explores different methodologies for risk evaluation and reliability assessment, which can be categorized into qualitative, quantitative, and hybrid qualitative and quantitative (HQQ) approaches. Qualitative methods include failure mode analysis, graphical analysis, and hazard analysis, while quantitative methods include analytical methods, stochastic methods, Bayes’ theorem, reliability optimization, multi-criteria analysis, and data utilization. HQQ methodology combines table-based and visual analysis methods. Currently, reliability assessment techniques such as mean time between failures (MTBF), system average interruption frequency index (SAIFI), and system average interruption duration index (SAIDI) are commonly used to predict PVPS performance. However, alternative methods such as economical metrics like the levelized cost of energy (LCOE) and net present value (NPV) can also be used. Therefore, a risk and reliability approach should be applied together to improve the accuracy of predicting significant aspects in the photovoltaic industry. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

Amid the increasing complexity of urban risks, urban parks not only serve ecological and recreational functions but are increasingly becoming a critical spatial foundation supporting public psychological resilience and social recovery. This study aims to systematically evaluate the daily adaptability of urban parks in the context of micro-risks. The research integrates the theories of “restorative environments,” environmental safety perception, urban resilience, and social ecology to construct a five-dimensional framework for perceived resilience, encompassing resilience, safety, sociability, controllability, and adaptability. Additionally, a dynamic feedback mechanism of perception–behavior–reperception is introduced. Methodologically, the study utilizes the Fuzzy Delphi Method (FDM) to identify 17 core indicators, constructs a causal structure and weighting system using DEMATEL-based ANP (DANP), and further employs the VIKOR model to simulate public preferences in a multi-criteria decision-making process. Taking three representative urban parks in Guangzhou as empirical case studies, the research identifies resilience and adaptability as key driving dimensions of the system. Factors such as environmental psychological resilience, functional diversity, and visual permeability show a significant path influence and priority intervention value. The empirical results further reveal significant spatial heterogeneity and group differences in the perceived resilience across ecological, neighborhood, and central park types, highlighting the importance of context-specific and user-adaptive strategies. The study finally proposes four optimization pathways, emphasizing the role of feedback mechanisms in enhancing urban park resilience and shaping “cognitive-friendly” spaces, providing a systematic modeling foundation and strategic reference for perception-driven urban public space optimization. Full article

Current non-destructive testing (NDT) methods, such as those based on wave velocity measurements, lack the sensitivity necessary to detect early-stage damage in concrete structures. Similarly, common signal processing techniques often assume linearity and stationarity among the signal data. By analyzing wave attenuation measurements using advanced signal processing techniques, mainly Hilbert–Huang transform (HHT), this work aims to enhance the early detection of damage in concrete. This study presents a novel energy-based technique that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Hilbert spectrum analysis (HSA), to accurately capture nonlinear and nonstationary signal behaviors. Ultrasonic non-destructive testing was performed in this study on manufactured concrete specimens subjected to micro-damage characterized by internal microcracks smaller than 0.5 mm, induced through controlled freeze–thaw cycles. The recorded signals were decomposed from the time domain using CEEMDAN into frequency-ordered intrinsic mode functions (IMFs). A multi-criteria selection strategy, including damage index evaluation, was employed to identify the most effective IMFs while distinguishing true damage-induced energy loss from spurious nonlinear artifacts or noise. Localized damage was then analyzed in the frequency domain using HSA, achieving an up to 88% reduction in wave energy via Marginal Hilbert Spectrum analysis, compared to 68% using Fourier-based techniques, demonstrating a 20% improvement in sensitivity. The results indicate that the proposed technique enhances early damage detection through wave attenuation analysis and offers a superior ability to handle nonlinear, nonstationary signals. The Hilbert Spectrum provided a higher time-frequency resolution, enabling clearer identification of damage-related features. These findings highlight the potential of CEEMDAN-HSA as a practical, sensitive tool for early-stage microcrack detection in concrete. Full article

Urban mobility resilience plays a central role in sustainable urban planning discussions, especially considering the challenges of extreme events, climate change, and the increasing scarcity of fossil fuels. This study evaluates urban mobility resilience in Petrópolis (RJ), incorporating socio-spatial heterogeneity and energy vulnerability. This research fills methodological gaps in the literature by proposing a composite resilience index that integrates technical, socioeconomic, and fossil fuel dependency variables within a robust multicriteria framework. We selected eleven variables relevant to urban mobility and organized them into inference blocks. We normalized the variables using Gaussian functions, respecting their maximization or minimization characteristics. We applied the Analytic Hierarchy Process (AHP) to assign weights to the criteria and then aggregated and ranked the results using multicriteria analysis. The final index represents the adaptive capacity of urban territories facing the energy crisis, and we applied it spatially to the neighborhoods of Petrópolis. The analysis identified a significant concentration of neighborhoods with low resilience, particularly in quadrants, combining deficiencies in public transportation, high dependence on fossil fuels, and socioeconomic constraints. Factors such as limited pedestrian access, insufficient motorized public transport coverage, and a high proportion of elderly residents emerged as significant constraints on urban resilience. Intervention strategies that promote active mobility, improve accessibility, and diversify transportation modes proved essential for strengthening local resilience. The results emphasize the urgent need for public policies to reduce energy vulnerability, foster active mobility, and promote equity in access to transportation infrastructure. Full article

A partially ordered set (or a poset, for short) is a set endowed with a partial order relation, i.e., with a reflexive, anti-symmetric, and transitive binary relation. As mathematical objects, posets have been intensively studied in the last century, coming to play essential roles in pure mathematics, logic, and theoretical computer science. More recently, they have been increasingly employed in data analysis, multi-criteria decision-making, and social sciences, particularly for building synthetic indicators and extracting rankings from multidimensional systems of ordinal data. Posets naturally represent systems and phenomena where some elements can be compared and ordered, while others cannot be and are then incomparable. This makes them a powerful data structure to describe collections of units assessed against multidimensional variable systems, preserving the nuanced and multi-faceted nature of the underlying domains. Moreover, poset theory collects the proper mathematical tools to treat ordinal data, fully respecting their non-numerical nature, and to extract information out of order relations, providing the proper setting for the statistical analysis of multidimensional ordinal data. Currently, their use is expanding both to solve open methodological issues in ordinal data analysis and to address evaluation problems in socio-economic sciences, from multidimensional poverty, well-being, or quality-of-life assessment to the measurement of financial literacy, from the construction of knowledge spaces in mathematical psychology and education theory to the measurement of multidimensional ordinal inequality/polarization. Full article

The increasing urgency of the energy transition, particularly in ecologically sensitive regions, demands spatially informed planning tools to guide renewable energy development. This study presents a Multi-Criteria Decision-Making (MCDM) approach to assess the suitability of the Gargano district in southern Italy for the implementation of Renewable Energy Communities. The analysis combines expert-based weighting and the Weighted Linear Combination method to evaluate seven key criteria grouped into environmental, socioeconomic, and technical dimensions. The resulting suitability scores, calculated at the municipal scale, highlight spatial disparities across the district, revealing that areas with the highest potential for Renewable Energy Community (REC) deployment are largely situated at the boundaries of the Gargano National Park. These zones benefit from stronger infrastructure, higher energy demand, and fewer environmental constraints, particularly with regard to wind energy initiatives. Conversely, municipalities within the park exhibit lower suitability, constrained by strict landscape regulations and lower population density. The findings provide valuable insights for regional planners and policymakers, supporting the adoption of targeted, environmentally compatible strategies for the advancement of citizen-led renewable energy initiatives in complex territorial contexts. Full article

To address the growing demand for industrial software in the digital transformation of small and medium-sized enterprises (SMEs) in the manufacturing sector, and to ensure the stable integration and operation of multi-source heterogeneous industrial software under complex conditions—such as heterogeneous compatibility, component dependencies, and uncertainty disturbances—this study established a comprehensive evaluation index system for service composition and optimal selection (SCOS). The system incorporated key criteria including service time, service cost, service reputation, service delivery quality, and availability. Based on this, a bi-objective SCOS model was established with the goal of maximizing both quality of service (QoS) and availability. To efficiently solve the proposed model, a hybrid enhanced multi-objective Gray Wolf Optimizer (HEMOGWO) was developed. This algorithm integrated Tent chaotic mapping and a Levy flight-enhanced differential evolution (DE) strategy. Extensive experiments were conducted, including performance evaluation on 17 benchmark functions and case studies involving nine industrial software integration scenarios of varying scales. Comparative results against state-of-the-art, multi-objective, optimization algorithms—such as MOGWO, MOEA/D_DE, MOPSO, and NSGA-III—demonstrate the effectiveness and feasibility of the proposed approach. Full article

Interest in explainable machine learning has grown, particularly in healthcare, where transparency and trust are essential. We developed a semi-automated evaluation framework within a clinical decision support system (CDSS-EQCM) that integrates LIME and SHAP explanations with multi-criteria decision-making (TOPSIS and Borda count) to rank model interpretability. After two-phase preprocessing of 2934 COVID-19 patient records spanning four epidemic waves, we applied five classifiers (Random Forest, Decision Tree, Logistic Regression, k-NN, SVM). Five infectious disease physicians used a Streamlit interface to generate patient-specific explanations and rate models on accuracy, separability, stability, response time, understandability, and user experience. Random Forest combined with SHAP consistently achieved the highest rankings in Borda count. Clinicians reported reduced evaluation time, enhanced explanation clarity, and increased confidence in model outputs. These results demonstrate that CDSS-EQCM can effectively streamline interpretability assessment and support clinician decision-making in medical diagnostics. Future work will focus on deeper electronic medical record integration and interactive parameter tuning to further enhance real-time diagnostic support. Full article

Climate change, the consequences of which have been more intense than ever in the last few decades, makes the need for sustainable transportation even more imperative. The promotion of public transportation and the discouragement of private car use are among the main priorities of sustainable transport planning in modern urban areas. However, the selection of the most appropriate transport project, apart from significant opportunities, is also accompanied by significant challenges, especially under the demand of compromising—often conflicting—social, environmental, and economic criteria, as well as different stakeholders’ interests. The aim of the present paper is to provide decision analysts and policy-makers with a decision-support tool for the prioritization and optimum selection of public transport projects for an urban area within the framework of sustainability. For this purpose, a comprehensive inventory of criteria for the evaluation of urban public transport systems (alternatives), along with a standardized table with the relevant performance of the most common alternatives (i.e., metro, tram, monorail, and BRT) are provided based on international literature review. A multi-criteria decision-aiding methodology based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), allowing for the direct exclusion of an alternative not meeting certain “binding” criteria from further evaluation, thus saving time, effort and cost, taking into account different stakeholders’ interests and preferences, as well as the particularities and special characteristics of the study area, is then proposed and tested through a theoretical case study. Full article

This study examines the degree of alignment between the actual environmental performance and the ESG disclosures of 365 listed financial institutions in Europe for the fiscal year 2024. Although ESG reporting has become a standard practice in the financial sector, there are still concerns that the quality of the disclosure may not accurately reflect substantive environmental action, which increases the risk of greenwashing. This study addresses this issue by incorporating both ESG disclosure indicators and green accounting metrics into a multi-criteria decision-making framework. This framework is supported by entropy-based weighting to assure objectivity in criterion importance, as outlined in the PROMETHEE II method. The Greenwashing Risk Index (GWI) is a groundbreaking innovation that quantifies the discrepancy between an institution’s classification based on ESG transparency and its performance in green accounting indicators, including environmental penalties, provisions, and resource usage. The results indicate that there is a substantial degree of variation in the performance of ESGs among institutions, with a significant portion of them exhibiting high disclosure scores but insufficient environmental substance. These discrepancies indicate that reputational sustainability may not be operationally sustained. The results have significant implications for regulatory supervision, sustainable finance policy, and ESG rating methodologies. The framework that has been proposed provides a replicable, evidence-based tool for identifying institutions that are at risk of greenwashing and facilitates the implementation of more accountable ESG evaluation practices in the financial sector. Full article